DSWorld: A Data Science World Model for Efficient Autonomous Agents· DSWorld：高效自主代理的数据科学世界模型
Despite strong capabilities in data understanding and decision-making, autonomous data science agents still heavily rely on trial-and-error workflows that involve expensive computation. This bottleneck motivates models that can anticipate the effects of data science operations before real execution. In this paper, we introduce the concept of Data Science World Model, which model the data science execution environment by predicting environment state transitions conditioned on current workflow states and candidate operations. We further propose DSWorld, a practical framework that combines structured state construction, cost-aware routing, lightweight real execution, and an LLM-based simulator for expensive operations. To support training, we construct an 8K-scale transition trajectory datase
提出 DSWorld 框架，减少数据科学自主代理的试错成本。
- 核心方法
- 通过构建数据科学世界模型（DSWorld），结合结构化状态构建、成本感知路由、轻量级真实执行和基于 LLM 的模拟器来预测操作效果。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 自主数据科学代理在执行任务时依赖于昂贵的试错流程，导致效率低下。
- 关键实验
- 构建了一个 8K 规模的转换轨迹数据集以支持训练。
- 主要贡献
- 引入数据科学世界模型的概念，并提出 DSWorld 框架以提高自主代理的执行效率。
- 意义与局限
- 该框架有助于提高数据科学任务中的自动化和效率，但其实际应用效果仍需进一步验证。